Comparing Stock Sector Metrics by Date Range
Introduction
The following horizontal bar graphs
compare sector performance with four analytical measures for each
election. The first is drawdown, which describes a fall in share or
portfolio price as a percentage of the peak price for the specified
time. The second is volatility, or the standard deviation of returns for
the period multiplied by the square root of the number of trading days,
estimated at 252 days each year. Multiplying the volatility by 100
allows the bar graphs to report them as percentages. Sectors with lower
volatility experience fewer price fluctuations, and vice versa.
The last two metrics, alpha, and beta,
compare sector performance to a benchmark index for the market. This
paper uses the S&P 500 index. The alpha metric reports how much
better or worse the sector did compared to the S&P 500. For example,
if the alpha is 3%, this sector earned 3% more than the S&P 500 did
for the period, the opposite is true for negative alpha values. Beta, on
the other hand, compares sector volatility to that of the benchmark
index. With the index fixed at 1, betas greater than 1 are considered
more volatile than the market, and those below 1 are less volatile than
the benchmark for that period.
To implement the calculation of these variables in R, several
packages are required:
60 Days before and After the 2020 Election
We can see there are significant changes between the two time
periods. The Tech sector has greatly improved performance, however, the
Healthcare sector does not exhibit the same improvements. We also see
that volatility in the financial sector continued to increase where
other sectors had decreases in their volatility.
30 Days before and after the 2020 Election
Comparing these time frames to the previous ones, we see an increase
in volatility for every sector except for healthcare. We also see the 30
days after measures decrease as it transitions into the 60 day mark.
Overall, the sectors are performing decently. There are likely some
improvements to be made.
15 Days before and after the 2020 Election
15 days after the election we see the financial volatility peak. This
value almost doubles from the volatility 15 days before the election. We
had expected to see strong performance in the Healthcare and Tech
sectors. The Tech sector improved, but the healthcare sector did
not.
Conclusion
Per Hypothesis I, some sectors do
experience increased volatility as proximity to the election increases,
primarily the financial and industrial sectors, but this is not seen
across the other sectors. Since Biden was the democratic nominee, the
tech and healthcare sectors are expected to experience greater
performance. The returns on the tech sector prices are greater than the
benchmark’s returns for the periods leading up to the election, however,
afterwards the returns fall below the market. This is not true for the
healthcare sector, whose returns are less than the benchmark for all
periods before and after the election. Beta values for the tech sector
indicate a decline in volatility compared to the market as the time
before the election decreased, eventually resulting in the volatility of
the sector falling below the benchmark’s volatility. Along with the
reduced drawdown before and after the election, these metrics indicate
strong performance in the tech sector. The betas for the healthcare
sector show slight increases in volatility before the election and clear
increases in volatility after the election. Healthcare drawdown
decreases closer to the election but then increases significantly after
the election. This sector did not experience the same positive
performance as the tech sector.
Using similar analytical interpretations
for the other two sectors, the financial sector had large swings in
volatility and drawdown after the election. The industrial sector’s
volatility peaked on election day but proceeded to decline steadily
afterward. Tech had the greatest improvement in performance compared to
the other sectors for this election.
